Author

Ke-Chao Tang

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#machine learning Preprint Oct 2026

ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems

Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead. Analog compute-in-memory (CIM) offers an energy-efficient alternative, yet its inherent hardware non-idealities perturb stored weights and can significantly de...

Wen-Shuai Yao, Wen-Yong Zhou, Han-Yong Shao et al. · 0 citations

Preprint Sep 2026

NOVA-CIM: Noise- and Correlation-Tolerant Stochastic Interfaces for Analog Compute-in-Memory

NoVA-CIM is presented, a noise- and correlation-tolerant stochastic interface for analog CIM by replacing multi-bit ADC readout with random-reference 1-bit sensing and reconstructing results through lightweight counting, which provides a unified robustness analysis showing that dynamic read noise is suppressed through...

Jia-Chen Ren, Wen-Shuai Yao, Hao-Bo Liu et al. · 0 citations

Jul 2026

Recall Before You Rank: Similarity-Guided Top-K Reuse for Efficient Long-Context Attention

ReTopK is a training-free method that accelerates dynamic Top-$K$ attention by reusing historical retrieval decisions and retains the complete KV cache and reuses only selected indices, rather than historical scores, attention weights, or outputs.

Wen-Shuai Yao, Wen-Yong Zhou, Hanyong Shao et al. · arXiv.org · 0 citations